Extant image recognition systems, such as classifiers, cascading classifiers, neural networks, convolutional neural networks, or the like are used to receive an image of an object and determine the identity of the object. These systems may achieve high accuracy (e.g., comparable to humans) when the received image contains high-quality attribute and pixel data. However, the accuracy of extant systems is decreased when low-quality images are used. This is due, in part, to conflicting classifications when different objects (such as a vehicle and a person, a kitten and a ball, or the like) are present within the image, and more importantly, a recognition system is unable to determine the differences in objects because of the quality of image attribute data.
In particular, such systems, which often comprise neural networks or convolutional neural networks, may detect a plurality of objects within an image, recognize classes of objects within the image, and assign several potential identifications of the classes based on confidence scores. However, such processes usually generate many (e.g., on the order of 1000 or more) identified classes, and a few adjustments to attributes of the image may significantly alter an identified class distribution of the process.
One solution for improving image recognition or object detection is “stress testing” such systems, that is, determining the impact of several attribute adjustments in a controlled manner by isolating a single attribute for a received and identified image, repeatedly adjusting the attribute from minor to extreme modifications, determining the recognition confidence (e.g. the object identification) for each adjustment, and repeating this process for each tested attribute. By doing so, it may be possible to build a baseline relating to image quality and attribute data. The data from stress testing may be used to create distribution and statistical analysis to further understand whether minor, or extreme, modifications improve the recognition systems so that image quality may be properly mitigated in future image object identification.
The present disclosure provides systems, methods, and devices to further improve the accuracy of such object recognition processes by stress testing image recognition models using modified images. By building a baseline and quality control parameters, the identification process is further able to account for image quality by modifying image attributes, such as rotation, focus, brightness, contrast, black/white, and color.